The idea of machines emulating human intelligence to perform tasks, make decisions, and improve their learning patterns was introduced to computer science in the 1950s [1]. Today, artificial intelligence (AI) is a highly-trending topic and a prominent part of our lives, from chatbots to digital phone assistants to smart homes. Its integration into our routine aside, AI plays a central role in life sciences, mainly biotechnology and bioinformatics, with the common goal of interpreting complex biological processes. AI algorithms are widely used to analyze big omics  data to identify drug targets as well as to predict the activity of drug candidates on their targets.

Given that post-translational modifications, such as glycosylation, add a new layer of complexity to analyzing protein-protein and protein-drug interactions, the application of bioinformatics to glycobiology is necessary to understand and may predict  the role of glycans in various forms of cellular behavior.

Early Implementation of AI in Glycobiology

The implementation of AI for glycomics began in the 1990s with mass spectrometry pipelines, where machine learning algorithms were applied to predict glycopeptide fragment intensities [2]. With the increased emphasis on protein glycosylation patterns, researchers wanted to characterize glycosylation sites in more detail by studying the amino acid sequence of N-glycosylation and the lesser-studied O-glycosylation. Although it was known that glycan linkage occurred at the oxygen of a serine or a threonine, the role of the neighboring amino acids on O-glycosylation   not been elucidated.

During the era of first-generation AI tools, datasets of glycosylation sites have been collected from proteins in tissue samples and biopsies, which were made available on databases such as UniPep [3] and N-GlycositeAtlas[4]. In addition, artificial neural network tools, such as NetNGlyc [5] and YinOYang [6] were developed to predict new N- and O- glycosylation sites using the known glycan data as training sets. Between 2005 and 2015, the predictive power of neural networks was improved through support vector machines and random forest algorithms. Based on these algorithms, software solutions like GlycoMine [7] used a multilayered prediction based on amino acid sequence, and structural and functional features of glycans to improve glycosylation site prediction.

Advancements In Machine Learning Algorithms for Glycosylation Analysis

 Today, the influence of AI on glycobiology continues to expand with the combination of genomics, transcriptomics, and proteomics, as well as computational methods, which greatly enhance site prediction and glycan profiling. For example, Moon et al. developed a random forest algorithm that takes steric and electronic parameters of glycan stereoisomers to accurately predict the selective binding of a particular isomer [8]. Antonakoudis et al. used artificial neural networks in a systems-based approach, where a stoichiometric model was developed to predict glycosylation enzyme fluxes and the subsequent glycan abundances [9].

Meanwhile, other platforms, such as Glycowork, focused on processing broad glycan data to reveal organism-specific glycan profiles [10].  

Besides site prediction and profiling, AI tools contributed to a better understanding of the complex relationship between glycans and cellular phenotypes. Qin et al. introduced an algorithm that uses single-cell SUGAR-seq data to predict the genes that led to N-glycan branching and the effect of different branches on T-cell subtypes in mouse models [12]. Interestingly, these genes were not uncovered in differential expression analysis between cell subtypes, which highlights the value of deep learning in phenotypic analysis.

Another exciting tool is GlyCompareCT, which – as its name suggests – compares the composition and abundance of glycan motifs in different datasets by decomposing them into glycan substructures [13]. This allows users to generate the complete set of motifs from the substructures. The Python-based nature of GlyCompareCT makes it a user-friendly tool that can be run via command-line.

Challenges and Future Directions in Glycoinformatics

While the multitude of glycoinformatics tools can contribute to our understanding of glycosylation, more work is needed to integrate next-generation machine learning into glycobiology. In particular, deep learning tools are instrumental when working with large and unstructured data sets. AlphaFold [14] is one of the pioneering projects that employs deep learning to predict protein structure, including its possible folded states. That said, the platform can only process protein sequences, thus lacking the foresight for glycosylation and other post-translational modifications.

More recently, deep learning methods began to be used for deducing glycosyltransferase structure and function from sequence data. Taujale et al. developed a workflow that used supervised deep learning to infer the folding state of glycosyltransferases from their protein sequences, which allowed them to predict their sugar donor specificities [15]. Subsequently, novel tools, such as GlyNet [16], SweetTalk [17], and glyBERT [18], began to emerge, with improved predictive value for the synthesis of branched and non-linear glycans. The same tools could also be applied to predict protein glycosylation sites [19].

One of the main challenges in glycobiology is the lack of broad glycomics data, which obscures the discovery of novel glycan structures. Next-generation AI models can overcome this issue by incorporating new features in addition to glycan structure. These features can be extracted from omics data that provide information about the upstream (e.g., precursor monosaccharides) and downstream processes (impact on signaling pathways). Since several glycans can share common synthetic steps or exhibit similar downstream effects, this knowledge can significantly enhance the scope of predicted glycans [20].

Finally, the consortium of machine learning tools can be leveraged to understand host-pathogen interactions. In particular, the ability to foresee cross-species transmission can help circumvent t he impact of future pandemics. Firstly, evaluating similar glycan structures across different species can reveal the host receptor-glycan interactions that allow viral entry to see which organisms are susceptible to viral invasion. It can also shed light on how pathogens use glycosylation to mimic host glycans to evade immune response. Furthermore, the combination of input, such as glycan similarity and phylogenetic distance – between humans and the animal studied – can inform us about the likelihood of pathogenic mutations that enable host switching towards humans. Preliminary models, such as SweetNet, leverage next-generation machine learning tools such as graph convolutional neural networks to identify glycan receptors on influenza and rotavirus while revealing binding specificities [21]. This approach can be extrapolated to several other viral proteins to explain how they are transmitted in humans.

Conclusion

Continuous development of AI models and integration of multi-omics could be invaluable for addressing various questions in glycobiology. These include but are not limited to glycosyltransferase structures, glycosylation sites on proteins, the impact of complex glycans on cellular function, pathogen-host interactions, and immuno-oncology (i.e., tumor microenvironment).  The collection of novel insights gained from AI models will help researchers conduct more targeted studies to understand the role of glycosylation in health and disease.

There are currently many open-source software tools and databases on glycoinformatics. The Glycoinformatics Consortium (GLIC) webinar series is a great place to learn about some of these tools, particularly for storing and processing glycan array data. The most noteworthy microarray databases and processing tools include CarbArrayART [22], Glycan Array Dashboard (GLAD) [23], CarboGrove [24], and the Glycan Array Data Repository [25]. In addition, LectinOracle [26] and Glycowork [10] are promising deep learning-based tools to predict protein glycan interactions. A review article by Li et al. perfectly summarizes the collection of additional resources for the computational evaluation of glycosylation [27].

“To learn more about glycans and lectins and how they can be utilized in your workflow to push forward immunology research, check out our Exploring the World of Glycobiology ebook. For other resources and tips and tricks, stay tuned to the SpeakEasy Science blog.  “

1. McCarthy, J., et al., A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 2006. 27(4): p. 12-12.
2. Elias, J.E., et al., Intensity-based protein identification by machine learning from a library of tandem mass spectra. Nature biotechnology, 2004. 22(2): p. 214-219.
3. Zhang, H., et al., UniPep-a database for human N-linked glycosites: a resource for biomarker discovery. Genome biology, 2006. 7(8): p. 1-12.
4. Sun, S., et al., N-GlycositeAtlas: a database resource for mass spectrometry-based human N-linked glycoprotein and glycosylation site mapping. Clinical proteomics, 2019. 16(1): p. 1-11.
5. Gupta, R., E. Jung, and S. Brunak, NetNGlyc 1.0 Server. Center for biological sequence analysis, technical university of Denmark available from: http://www. cbs. dtu dk/services/NetNGlyc, 2004.
6. Gupta, R. and S. Brunak, Prediction of glycosylation across the human proteome and the correlation to protein function, in Biocomputing 2002. 2001, World Scientific. p. 310-322.
7. Li, F., et al., GlycoMine: a machine learning-based approach for predicting N-, C-and O-linked glycosylation in the human proteome. Bioinformatics, 2015. 31(9): p. 1411-1419.
8. Moon, S., et al., Predicting glycosylation stereoselectivity using machine learning. Chemical Science, 2021. 12(8): p. 2931-2939.
9. Antonakoudis, A., et al., Synergising stoichiometric modelling with artificial neural networks to predict antibody glycosylation patterns in Chinese hamster ovary cells. Computers & Chemical Engineering, 2021. 154: p. 107471.
10. Thomès, L., R. Burkholz, and D. Bojar, Glycowork: A Python package for glycan data science and machine learning. Glycobiology, 2021. 31(10): p. 1240-1244.
11. Bojar, D., et al., A useful guide to lectin binding: machine-learning directed annotation of 57 unique lectin specificities. ACS chemical biology, 2022. 17(11): p. 2993-3012.
12. Qin, R., L.K. Mahal, and D. Bojar, Deep learning explains the biology of branched glycans from single-cell sequencing data. Iscience, 2022. 25(10).
13. Zhang, Y., et al., Preparing glycomics data for robust statistical analysis with GlyCompareCT. STAR protocols, 2023. 4(2): p. 102162.
14. Varadi, M., et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic acids research, 2022. 50(D1): p. D439-D444.
15. Taujale, R., et al., Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases. Elife, 2020. 9: p. e54532.
16. Carpenter, E.J., et al., GlyNet: a multi-task neural network for predicting protein–glycan interactions. Chemical Science, 2022. 13(22): p. 6669-6686.
17. Bojar, D., et al., Deep-learning resources for studying glycan-mediated host-microbe interactions. Cell Host & Microbe, 2021. 29(1): p. 132-144. e3.
18. Dai, B., D.E. Mattox, and C. Bailey-Kellogg, Attention please: modeling global and local context in glycan structure-function relationships. bioRxiv, 2021: p. 2021.10. 15.464532.
19. Taherzadeh, G., et al., SPRINT-Gly: predicting N-and O-linked glycosylation sites of human and mouse proteins by using sequence and predicted structural properties. Bioinformatics, 2019. 35(20): p. 4140-4146.
20. Bao, B., et al., Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis. Nature communications, 2021. 12(1): p. 4988.
21. Burkholz, R., J. Quackenbush, and D. Bojar, Using graph convolutional neural networks to learn a representation for glycans. Cell Reports, 2021. 35(11).
22. Akune, Y., et al., CarbArrayART: a new software tool for carbohydrate microarray data storage, processing, presentation, and reporting. Glycobiology, 2022. 32(7): p. 552-555.
23. Mehta, A.Y. and R.D. Cummings, GLAD: GLycan Array Dashboard, a visual analytics tool for glycan microarrays. Bioinformatics, 2019. 35(18): p. 3536-3537.
24. Klamer, Z.L., et al., CarboGrove: a resource of glycan-binding specificities through analyzed glycan-array datasets from all platforms. Glycobiology, 2022. 32(8): p. 679-690.
25. York, W.S., et al., GlyGen: computational and informatics resources for glycoscience. Glycobiology, 2020. 30(2): p. 72-73.
26. Lundstrøm, J., et al., LectinOracle: A Generalizable Deep Learning Model for Lectin–Glycan Binding Prediction. Advanced Science, 2022. 9(1): p. 2103807.
27. Li, H., A.W. Chiang, and N.E. Lewis, Artificial intelligence in the analysis of glycosylation data. Biotechnology Advances, 2022. 60: p. 108008.

The body’s autoimmune response has been leveraged by cancer researchers to propel immunotherapy tools into clinical use. Although the main focus has been on the ability of T-cells to fight cancer cells, the involvement of tumor-infiltrating B-cells is also becoming evident. Tumor-infiltrating B lymphocytes (TIL-Bs) are produced in much higher amounts in various cancers than in healthy tissues, which highlights their positive prognostic value (1). However, their exact role in cancer remains controversial, with the demonstration of both positive and negative impacts.

While they play a critical role in the recruitment of other immune cells and the production of tertiary lymphoid structures to enhance antitumor immunity, some B-cell types have also been shown to promote inflammation and suppress the immune response (2). The conflicting evidence of TIL-B action in cancer points to the need to better understand how B-cells develop and interact with their targets. 

One way B-cells promote immune response is through the production of antibodies for antigen recognition. It is interesting to note that many of these glycoantigens, such as MUC1 (3) and ganglioside D3 (4), are aberrantly glycosylated in cancer. That’s why investigating their interactions with glycans can enlighten us about the role of glycosylation in the development, differentiation, maturation, and survival of B-cells. 

B-cell development 

Germinal center (GC) B-cell development and differentiation are prerequisites for adaptive immune response through B-cell-mediated antibody production. Initially, naive B-cells found in the spleen, tonsil, and primary lymph nodes are exposed to the antigens on the surface of dendritic cells, which leads to their activation.

Activated B-cells form clusters of germinal centers in primary follicles, where they proliferate, mature, and differentiate. More specifically, they undergo a process called somatic hypermutation, defined as the introduction of random mutations to the B-cell receptor regions. Thus, the body generates a library of B-cells with highly diverse receptors with an affinity to various antigens.

These B-cells are filtered according to their interactions with the antigens displayed on the surface of follicular dendritic cells. Depending on the antigens presented, the B-cells undergo class switch recombination to differentiate into plasma cells or memory B-cells, which are responsible for the secretion of antibodies in the bloodstream. 

As we zoom into each step of this process, we encounter glycan-lectin interactions that promote or regulate B-cell development at various points. 

Galectins modulate B-cell development 

Recent studies uncovered the role of galectins in regulating the adaptive immune response. It was shown that galectins can interact with the surface glycans of both innate and adaptive immune cells. Galectin interactions play a multifaceted role in modulating B-cell activation and germinal center-type differentiation essential for maintaining immune response (5).    

Gal-1 was implicated as a promoter of B-cell receptor-mediated signaling, which increased B-cell activation and proliferation (6). On the other hand, Gal-1 was also shown to interact with immune receptors, such as CD45—which is involved in B-cell activation and survival, to modulate activation (7). Similarly, Gal-3 and Gal-9 reduced activation and GC development rates in splenic and tonsillar B-cells, respectively (8–9).  

Furthermore, Galectins are one of the key players in the differentiation fate of B-cells. For example, Gal-1 and Gal-8 can be effective promoters of B-cell differentiation, driving the early stages of immunoglobulin production (10) or establishing plasma cell homeostasis (11). In other studies, Gal-1 was shown to induce apoptosis in active B-cells to terminate immune response (12).

Further research also revealed that the modulatory activity of galectins was dependent on the surface glycan profiles of B-cells. For example, Giovannone et al. demonstrated the role of the B-cell N-glycan composition on Gal-9.

Although Gal-9 strongly bound naive memory B-cells to blunt their activity and exhibited strong binding, GC B-cells exhibited diminished Gal-9 binding due to the I-branching of their surface glycoproteins (9). Thus, a bidirectional relationship between the glycan profiles of B-cells and the level of suppression through galectin binding becomes clear.  

O-linked glycans and B-cell differentiation 

The dynamic nature of GC B-cell glycan profiles was further investigated to uncover the role of glycan remodeling in B-cell differentiation.  

O-glycan modifications were previously identified in T cells, where altered T-antigen expression on the CD8 glycoprotein was associated with changes in T-cell activity. The T-antigen levels of T cells at different maturity levels were detected by peanut agglutinin (PNA) binding.

Following this strategy, Giovannone et al. exposed GC B-cells to PNA to find that α2,3 sialyltransferase, ST3GAL1, was downregulated, which altered the B-cell receptor-type tyrosine phosphatase CD45 (13). Furthermore, using a series of plant lectins, the research team demonstrated that ST3GAL1 overexpression nullified PNA binding in differentiated GC B-cells and shifted the glycan composition of CD45.

Indeed, the cells exhibited a shift from extended core-2 O-glycans to truncated α2,3-sialylated T-antigen, as determined by binding affinity studies with plant lectins from Vector Laboratories, such as Maackia amurensis lectin-II (MAL-II), Jacalin, Phaseolus vulgaris leucoagglutinin (PHA-L), and Sambucus nigra agglutinin (SNA). Overall, the study established a framework of alterations in GC B-cell glycosylation and glycan profiles at different stages of differentiation.   

N-linked glycans and B-cell maturation 

Negative selection is an obstacle before cell maturation, whereby the binding of high-affinity self-antigens to B-cells can promote cell death. As previously mentioned, N-glycan branching on B-cells is a determining factor for whether or not galectins can bind and inhibit their maturation. This raises more questions about the influence of N-glycans on B-cell survival.

To that end, Mortales et al. provided further evidence that N-glycan branching is required for mature B-cell development (14). The team employed a flow cytometry protocol to detect glycan expression profiles on bone marrow B-cells stained with fluorophore-conjugated Phaseolus vulgaris leukoagglutinin (L-PHA) from Vector Laboratories. B-cells with branched N-glycans on CD19 displayed heightened signaling through their antigen receptors, stimulating positive selection and maturation. In contrast, suppressed N-glycan branching in pre-B-cells promoted a negative cell selection and eventual apoptosis.  

Interestingly, N-glycan branching can also create a seesaw effect between innate and adaptive immune responses in specific conditions. Another study by Mortales et al. showed that branching reduced pro-inflammatory innate responses by triggering the endocytosis of toll-like receptors TLR2 and TLR4, fostering adaptive immunity through increased B-cell receptor signaling in multiple sclerosis (15). 

Sialic Acid and B-cell survival 

Sialic acids are abundant on B-cell surfaces. In particular, sialoglycans have previously been associated with B-cell signaling and migration through interacting with Sialic acid binding immunoglobulin-type lectins (Siglecs). To better elucidate the role of sialic acid-siglec interactions, Linder et al. blocked sialic acid synthesis in mouse models, expecting a dampening of cell migration.

However, sialoglycan deficiency also influenced the B-cell population, unlike sialylated glycoprotein expression, which had negligible influence on cell fate. In contrast, sialoglycan deficiency caused an increase in apoptotic signals by caspase 3 and 8, giving rise to B-cell deficient mice (16). The study highlights the importance of these results for cancer cells, emphasizing the protective roles of sialoglycans against cancer cell apoptosis and implicating them as potential therapeutic targets. 

Conclusion and future directions 

The growing pool of evidence agrees on the regulatory role of glycosylation in B-cell development, differentiation, maturation, and survival. Lectin interactions with B-cell surface glycans are determining factors for the route B-cell development will follow. Therefore, future studies must connect glycomics with other omics data to improve the breadth of knowledge on glycosylation and adaptive immune response.  

Live-cell imaging has proven to be critical in evaluating the role of glycans in morphology and cellular interactions. However, the commonly used enzyme labeling methods for glycan detection in live cells still lack specificity and localization while potentially exerting cytotoxicity on the cells.

Click chemistry tools that recently gained recognition with the Nobel Prize in Chemistry 2022 hold immense potential to simplify glycoprotein labeling. More specifically, glycan epitopes of proteins are replaced with derivatives of naturally occurring monosaccharides that can bind reporter molecules through straightforward click reactions. Thus, bioconjugation of clickable monosaccharides to reporters offers non-invasive, quick, and robust glycan labeling with higher specificity (17). We will cover click chemistry-aided bioconjugation methods in more detail in future blog posts. 

Another exciting future direction in glycobiology is the integration of machine learning-aided statistical and bioinformatics tools. Thanks to the wide range of experimental data available on web interfaces, genome-wide data acquisition has been made possible.

Thus, the variety of transcriptomic and metabolomic data can be correlated to glycomics to demonstrate a holistic view of the relationship between glycans and B-cell development (18). This can be especially helpful in unlocking the multifaceted roles of B-cells in cancer progression and immune responses, which will inform the development of more effective immunotherapy strategies. 

To learn more about glycans and lectins and how they can be utilized in your workflow to push forward immunology research, check out our Exploring the World of Glycobiology ebook. For other resources and tips and tricks, stay tuned to the SpeakEasy Science blog.   

1. Laumont CM, et al. 2022. Tumour-Infiltrating B Cells: Immunological Mechanisms, Clinical Impact and Therapeutic Opportunities. Nature Reviews Cancer. 
2. Wei X, et al. 2015. Regulatory B Cells Contribute to the Impaired Antitumor Immunity in Ovarian Cancer Patients. Tumor Biology. 
3. Pavoni E, et al. 2007. Tumor-Infiltrating B Lymphocytes as an Efficient Source of Highly Specific Immunoglobulins Recognizing Tumor Cells. BMC Biotechnology. 
4. Kotlan B, et al. 2005. Novel Ganglioside Antigen Identified by B Cells in Human Medullary Breast Carcinomas: The Proof of Principle Concerning the Tumor-Infiltrating B Lymphocytes. The Journal of Immunology. 
5. Liu FT, et al. 2023. The Role of Galectins in Immunity and Infection. Nature Reviews Immunology. 
6. Gauthier L, et al. 2002. Galectin-1 is a Stromal Cell Ligand of the Pre-B Cell Receptor (BCR) Implicated in Synapse Formation Between Pre-B and Stromal Cells and in Pre-BCR Triggering. Proceedings of the National Academy of Sciences. 
7. Yu X, et al. 2006. Interaction of the B Cell-Specific Transcriptional Coactivator OCA-B and Galectin-1 and a Possible Role in Regulating BCR-Mediated B Cell Proliferation. Journal of Biological Chemistry. 
8. Beccaria CG, et al. 2018. Galectin-3 Deficiency Drives Lupus-Like Disease by Promoting Spontaneous Germinal Centers Formation via IFN-γ. Nature Communications. 
9. Giovannone N, et al. 2018. Galectin-9 Suppresses B Cell Receptor Signaling and is Regulated by I-Branching of N-Glycans. Nature Communications. 
10. Tsai CM, et al. 2008. Galectin-1 Promotes Immunoglobulin Production During Plasma Cell Differentiation. The Journal of Immunology. 
11. Anginot A, et al. 2013. Galectin 1 Modulates Plasma Cell Homeostasis and Regulates the Humoral Immune Response. The Journal of Immunology. 
12. Tabrizi SJ, et al. 2009. T Cell Leukemia/Lymphoma 1 and Galectin-1 Regulate Survival/Cell Death Pathways in Human Naive and IgM+ Memory B Cells Through Altering Balances in Bcl-2 Family Proteins. The Journal of Immunology. 
13. Giovannone N, et al. 2018. Human B Cell Differentiation is Characterized by Progressive Remodeling of O-linked Glycans. Frontiers in Immunology. 
14. Mortales CL, et al. 2020. N-Glycan Branching is Required for Development of Mature B Cells. The Journal of Immunology. 
15. Mortales CL, et al. 2020. N-Glycan Branching Decouples B Cell Innate and Adaptive Immunity to Control Inflammatory Demyelination. Iscience. 
16. Linder AT, et al. 2022. Sialic Acids on B Cells are Crucial for Their Survival and Provide Protection Against Apoptosis. Proceedings of the National Academy of Sciences. 
17. Wu ZL, et al. 2015. Glycoprotein Labeling with Click Chemistry (GLCC) and Carbohydrate Detection. Carbohydrate Research. 
18. Vicente MM, et al. 2023. Glycome Dynamics in T and B Cell Development: Basic Immunological Mechanisms and Clinical Applications. Trends in Immunology. 

Cells and cell populations function in a concerted manner through the communication between various macromolecules and subcellular compartments. Glycans are one of the main elements of cooperation in cellular mechanisms. Their vastly diverse roles include protein folding and stability, signal transduction, intracellular protein trafficking, cell differentiation, and immune response.

In the same breath, aberrant glycosylation can result in abnormal glycan structures with devastating effects. Disruptions in mechanisms of cell development, differentiation, signaling, and division are all recipes for some of the most hard-to-master diseases. In particular, rare congenital disorders and cancers possess disease phenotypes that are hard to fathom by simply looking at genetic mutations. To understand how cellular mechanisms are tweaked in the first place, we need to take a closer look at the glycan attached to the essential proteins that orchestrate homeostasis. 

Here we provide an overview of the role of glycans in healthy cell machinery as well as examples of altered glycans in congenital diseases and cancer. 

Glycans in Healthy Cellular Mechanisms 

The main cellular functions of glycans can be divided into three classes (you can look at our previous blog post for an introduction to the universe of glycans): structural/modulatory roles, intrinsic recognition, and extrinsic recognition. The structural/modulatory roles of glycans encompass a series of functions that help maintain cell integrity, membrane organization, and diffusion while shielding the cell through protective barriers.

Intrinsic recognition is central to the transport of nutrients as well as intracellular protein trafficking for signaling and degradation. Finally, extrinsic recognition involves robust pathogen identification and immune response by recognizing pathogen-specific glycans. Examples of these functions can be seen more evidently in specific cell types. 

One of the most noticeable cases is the role of glycans in neural function. In particular, N-glycans were found to be significant for the transmission of electric impulses and chemical neuromodulators.

Research shows that sialylated N-glycans are primary components of voltage-gated ion channels and are necessary for axon firing (1). Additionally, they are of central importance in the proper function of glial cells responsible for supporting neurons and establishing homeostasis in the central nervous system. More specifically, proteins that are responsible for glutamate transport (2) and blood-brain barrier formation (3) are N-glycosylated. 

Another case in point involves platelets responsible for blood clotting. Recent studies unveiled the role of glycosylation in platelet production and count. Animal studies show that the absence of glycosyltransferases, such as ST3Gal4 (4) and β4GalT1 (5), could lead to low platelet counts, indicating the importance of sialylation for platelet production. Even after platelet production, the importance of glycosylation remains, as the lack of sialylation was shown to cause premature platelet clearance (6).  

The role of glycans in hormonal balance is yet another area of exploitation and can unravel their influence on many physiological processes, from appetite to anxiety and alertness. A close look at biologically active peptide hormones reveals that one-third of peptide hormones identified in the human body are O-glycosylated. More importantly, these glycopeptides are necessary for facilitating receptor interactions and extending peptide half-lives (7). 

The Disease of Glycosylation: Altered Cellular Function 

Through examples from various specialized cell types, the role of glycosylation in a functioning cell became evident. When we turn the table, we also find that aberrant glycosylation is a driving factor in the onset of rare diseases and cancers. 

Congenital Disorders of Glycosylation 

Congenital Disorders of Glycosylation are a specific set of hereditary disorders that modify glycosylation networks. They are often caused by the absence of one or more enzymes. The Genetic and Rare Diseases Information Center (GARD) has identified 19 such diseases while indicating that approximately 50,000 people in the U.S. suffer from them. 

GALNT2 is a gene encoding for the member of glycosyltransferases that initiate O-glycosylation in mucin-type peptides that are of central importance to tissue development and metabolism. Among the many functions of this gene, the regulation of high-density lipoprotein cholesterol (HDL-C) stands out.

Often called “good” cholesterol, HDL is responsible for the transfer of low-density lipoprotein—a.k.a. bad—cholesterol (LDL-C) to the liver, thus mediating its clearance. Patients with low HDL-C levels have an increased propensity for heart disease and stroke due to LDL-C accumulation.

Gene knockout studies revealed a striking relationship between GALNT2 and HDL-C levels. In mice harboring GALNT2 loss-of-function, the phospholipid transfer protein responsible for LDL-C uptake from tissues was less active (8).

Another possible consequence of GALNT2 deficiency was recently revealed. Loss-of-function studies in mice helped identify a novel congenital disorder characterized not only by low HDL-C but also by intellectual disability, impaired cognitive function, epilepsy, and developmental defects, among many other debilitating symptoms (9). The effect of GALNT2 mutations on neurodevelopment continues to be investigated.  

GALNT3 is a similar protein-encoding gene that gives rise to UDP-GalNAc transferase 3, necessary for O-glycosylation initiation. A sequence analysis study from 2004 showed that the deletion of this gene was highly associated with a rare metabolic disorder called familial tumoral calcinosis (FTC) (10).

Individuals suffering from FTC often possess abnormal phosphate and calcium deposits around joints, severely hindering joint movement. Studies also showed that GALNT3 deficiency resulted in the inhibition of the glycosylation of fibroblast growth factor 23, which is responsible for calcium and phosphate transfer between tissues and the kidney.  

GALNT2 and GALNT3 are two examples of severe cases where the absence of a particular glycosyltransferase can manifest in devastating ways in the body.  

Cancer and Glycosylation 

The role of glycosylation in cancer cannot be overstated. The variety of genetic mutations aside, the cancerous phenotype is also driven by the mechanistic changes inside the cell and in the extracellular matrix (ECM). Research shows that changes in the surface glycan content are associated with increased stiffness of the cell and the extracellular matrix that biases the cells’ mechanotransduction pathways into cancerous phenotype (11). This improves the ability of cancer cells to proliferate, differentiate, adhere to ECM, and migrate.   

Proteoglycans seem to possess a prominent role in mediating the cancer response to mechanical cues from the ECM. For example, an increased level of heparan sulfate proteoglycans (HSPGs), such as agrin, promotes the assembly of substrate adhesion molecules that attach cancer cells to the ECM (12). Another transmembrane HSPG called syndecan-4 was shown to change conformation in response to mechanical stiffening, promoting the binding of scaffold proteins and activating the kindlin-integrin-RhoA pathway involved in adhesion to surrounding tissues (13). 

Transmembrane proteins, such as mucin, are heavily glycosylated. Mucin is a critical component of cellular integrity, as it often serves as a protective barrier. Research suggests a staggering increase of truncated O-glycans in cell surface MUC1 and MUC16. It is revealed that altered glycosylation changes the surface distribution of mucins. In healthy cells, mucins are sequestered from receptor tyrosine kinases, and their interactions are limited.

Through aberrant glycosylation, mucins get uniformly distributed throughout the entire cell surface and come into contact with kinases more frequently, which results in the overactivation of kinases and their downstream binding partners. Furthermore, this redistribution makes cancer cells resistant to degradation (14).  

You can look at the SpeakEasy blog to learn more about the specific downstream effects of glycan alterations in the tumor microenvironment and in cancer progression.  

Conclusion 

Protein glycosylation patterns can greatly determine the mechanistic properties and dynamic behavior of cells. Abnormalities can drive malignant behavior, such as uncontrolled signal transduction, immune checkpoint evasion, and ECM-mediated metastasis. That’s why profound strategies are required to study glycosylation at the single-cell level. An emerging technique is the application of transcriptomics to investigate cellular glycosylation.

One such platform is Glycopacity, which can run single-cell RNA-seq to identify 214 glycosylation-related enzymes and their RNA-level regulations (15). In addition, Surface-protein Glycan and RNA sequencing (SUGAR-seq) integrates glycosylation profiles and single-cell RNA-seq data, empowering discovery of novel biology. This method was employed to identify unique glycan epitopes in tumor-infiltrating T cells (16). These breakthroughs can pave the way for understanding the role of glycosylation in health and disease more precisely. 

With the help of research initiatives, our understanding of glycosylation will continue to expand. To that end, the National Institutes of Health (NIH) initiated a Glycoscience program to develop reliable glycobiology resources, assay kits, and high-throughput screening tools to assist researchers. The program also aims to develop integrative data analysis tools to make glycan analysis more streamlined.  

Last but not least, our SpeakEasy Science Blog and Glycobiology eBook serve as scientific hubs where you can find practical information about glycan detection and quantification as well as reviews of peer-reviewed articles exploring the roles of glycans in health and disease.  

References

1. Kruger LC, et al. 2016. Voltage-Gated Na+ Channels: Not Just for Conduction. Cold Spring Harbor Perspectives in Biology. 
2. Bauer D, et al. 2010. Abnormal Glycosylation of EAAT1 and EAAT2 in Prefrontal Cortex of Elderly Patients With Schizophrenia. Schizophrenia Research. 
3. Jing B, et al. 2018. Glycosylation of Dentin Matrix Protein 1 is a Novel Key Element for Astrocyte Maturation and BBB Integrity. Protein & Cell. 
4. Grozovsky R, et al. 2014. The Ashwell-Morell Receptor Regulates Hepatic Thrombopoietin Production via JAK2-STAT3 Signaling. Nature Medicine. 
5. Giannini S, et al. 2020. β4GALT1 Controls β1 Integrin Function to Govern Thrombopoiesis and Hematopoietic Stem Cell Homeostasis. Nature Communications. 
6. Greenberg J, et al. 1975. Effects on Platelet Function of Removal of Platelet Sialic Acid by Neuraminidase. Laboratory Investigation; A Journal of Technical Methods and Pathology. 
7. Madsen TD, et al. 2020. An Atlas of O-linked Glycosylation on Peptide Hormones Reveals Diverse Biological Roles. Nature Communications. 
8. Khetarpal SA, et al. 2016. Loss of Function of GALNT2 Lowers High-Density Lipoproteins in Humans, Nonhuman Primates, and Rodents. Cell Metabolism. 
9. Zilmer M, et al. 2020. Novel Congenital Disorder of O-linked Glycosylation Caused by GALNT2 Loss of Function. Brain. 
10. Topaz O, et al. 2004. Mutations in GALNT3, Encoding a Protein Involved in O-Linked Glycosylation, Cause Familial Tumoral Calcinosis. Nature Genetics. 
11. Purushothaman A, et al. 2023. The Role of Glycans in the Mechanobiology of Cancer. Journal of Biological Chemistry. 
12. Chakraborty S, et al. 2015. An Oncogenic Role of Agrin in Regulating Focal Adhesion Integrity in Hepatocellular Carcinoma. Nature Communications. 
13. Chronopoulos A, et al. 2020. Syndecan-4 Tunes Cell Mechanics by Activating the Kindlin-Integrin-RhoA Pathway. Nature Materials. 
14. Kufe DW. 2012. MUC1-C Oncoprotein as a Target in Breast Cancer: Activation of Signaling Pathways and Therapeutic Approaches. Oncogene. 
15. Dworkin LA, et al. 2022. Applying Transcriptomics to Study Glycosylation at the Cell Type Level. iScience. 
16. Kearney CJ, et al. 2021. SUGAR-seq Enables Simultaneous Detection of Glycans, Epitopes, and the Transcriptome in Single Cells. Science Advances. 

The traditional approach to disease biology typically involves collecting causes, symptoms, and treatments under one umbrella. Current therapeutics are designed to target the hallmarks of a disease, such as an undesired protein-protein interaction or excessive activity of an enzyme.

Target information is often extracted from a set of biomarkers, measurable indications of the disease state. However, the patient profile is too diverse to do this for many diseases. Especially in cancer and neurodegenerative diseases, we cannot underestimate the number of patients who do not respond to traditional small-molecule drug treatments. 

The failure of current small-molecule drug treatments in patients points to the need for an adequate biomarker portfolio. We need to dig deeper to find more distinct biomarkers for detailed diagnosis, prediction of treatment outcomes, and the implementation of personalized approaches, such as immunotherapy. In particular, post-translational modifications (PTMs) must be prioritized due to their roles in diversifying the functional space of proteins. 

Glycosylation is one of the most common and perplexing PTMs. The glycosylation of a macromolecule determines its fate in many aspects, from its subcellular localization to its potential binding partners. Glycosylation can occur in numerous combinations, giving rise to a diverse glycan profile, which could greatly vary between healthy and diseased states. This means that differential glycan profiles can be analyzed to identify disease states more accurately. 

Here, we give examples of how glycans can be exploited as robust biomarkers. 

Glycan Classes and Their Potentials as Biomarkers 

As we previously discussed in our blog post, “An Introduction to the Universe of Glycans”, glycans are classified according to the biomolecule they are attached to. All 3 classes—glycoproteins, proteoglycans, and glycolipids, contain strong indicators of disease.  

Many FDA-approved protein-based cancer biomarkers are glycoproteins, including the CA 125 antigen for ovarian cancer, CA 19-9 for pancreatic cancer, and CA 15-3 for breast cancer (1–3). Studying the glycosylation trend of these antigens can improve their diagnostic value in cancer.  

Heavily-glycosylated proteoglycans can be particularly valuable as metastatic cancer biomarkers. Circulating tumor cells (CTCs) are prevalent in liquid biopsies, and their metastatic properties are bestowed by proteoglycans (4). Furthermore, the sulfation of the glycosaminoglycan chains varies across different cancer types, triggering metastatic events such as angiogenesis, epithelial-to-mesenchymal transition, and movement across the blood vessel wall (5). 

Glycosphingolipids, particularly gangliosides, emerged as biomarkers for brain pathologies since they constitute 80% of the glycan mass in the brain (6). Changes in ganglioside levels are strongly associated with many neurodegenerative diseases, including epilepsy, stroke, multiple sclerosis (MS), Parkinson’s disease (PD), Huntington’s disease, and Alzheimer’s disease (AD) (7). 

Glycan Biomarkers in Disease 

Cancer 

A growing body of research reveals the bidirectional relationship between glycosylation patterns and cancerous behavior. Altered glycosyltransferase expression changes the surface glycan composition and triggers signaling cascades that transform cells into malignant phenotypes. Such cells contain an increased abundance of cancer-associated glycan motifs, such as truncated O-glycans, branched N-glycans, sialyl lewis antigens, and core fucosylation (8).  

In addition, cancer cells can modulate their glycosylation patterns depending on the conditions of their tumor microenvironment. The most obvious example is the changes in glycans as cancer progresses from an initial stage—with uncontrolled proliferation—to an advanced stage where metastasis occurs due to hypoxic conditions. The cells can adapt by attaining a distinct glycan profile to prepare for metastasis and to protect themselves against immune response and insufficient oxygen supply. 

In other words, glycans are valuable assets for biomarker studies to not only discern between healthy and cancerous cells but also determine disease prognosis and subtype. The expansion in the cancer biomarker portfolio can drive accurate and timely diagnosis of cancer types and stages.

Among them, prostate cancer presents particular diagnostic challenges due to the weak specificity of conventional prostate-specific antigens (PSAs). Efforts to identify and measure glycans attached to the PSA can improve the accuracy of detection tools. For example, researchers from the Hirosaki University Graduate School of Medicine in Japan designed an immunoassay system to differentiate between benign and malignant cell types in the prostate.  

They successfully differentiated between α2,3-linked sialyl N-glycan-carrying PSA and benign-associated α2,6-linked sialyl N-glycan-carrying PSA using the plant lectin Maackia amurensis, which specifically binds the former (9). 

Single glycan biomarkers are insufficient to encompass the complexity of the multiple reactions participating in cancer phenotypes. Current research emphasizes glycoprotein panels through liquid chromatography-mass spectrometry (LC-MS/MS) to simultaneously identify and quantify multiple glycoproteins. Thanks to the advancements in LC-MS/MS methods, researchers can even detect cancer-associated glycopeptides and proteins in very low relative abundance (10). 

Neurodegenerative Disorders 

The role of glycosylation in the central nervous system (CNS) came to light as scientists could investigate the neural proteome more elaborately. Today, studies uncover strong associations between glycogenes and Alzheimer’s disease, multiple sclerosis, Parkinson’s disease, and Huntington’s disease. 

Studies implicate P-glycoprotein as a potential biomarker and target in Alzheimer’s and Huntington’s disease, where the overabundance of the P-glycoprotein was strongly correlated with the accumulation of amyloid-β peptide and mutant huntingtin protein, respectively (11,12). 

Multiple sclerosis research focuses on myelin oligodendrocyte glycoprotein (MOG) and its role in disease progression. For example, Khare et al. showed that MS-associated antibodies induced autoimmune brain inflammation by binding MOG (13). Furthermore, MOG was shown to stimulate inflammatory cytokine production by T-cells (14). 

Multiple glycoproteins are at play in Parkinson’s disease. Dopaminergic dysfunction and neuronal degeneration have been linked to synaptic vesicle glycoprotein 2, the transmembrane glycoprotein GPNMB, and myelin-associated glycoprotein (15–17). 

In summary, identifying and quantifying the above glycoproteins—and many others—in the CNS can generate valuable insights into the onset of neurodegenerative systems. For example, one study employed biotinylated Agaricus bisporus lectin to detect the Gal-(beta-1,3)-GalNAc motif in microtubule-associated protein (MAP6). They unveiled hyperglycosylation of MAP6 in PD mouse models compared to the control model, which affirms the potential of MAP6 glycosylation as a robust PD biomarker (18). 

Aging  

Life expectancy has drastically increased since the 20th century. This caused the emergence of age-related diseases as the human body inevitably deteriorates and long-term effects of harmful habits like alcohol and processed food consumption manifest over time. 

Age is a risk factor for cancer and neurodegenerative diseases, but other age-related diseases and chronological aging can involve similar mechanisms in glycosylation. 

The N-glycome was shown to be particularly sensitive to chronological aging. Analysis of serum proteins revealed a general increase in agalactosyl and non-sialylated N-glycans and a decrease in core-fucosylated biantennary N-glycans (19). Furthermore, the course of glycan changes was subtle before 50 and more rapid afterwards (20). These results prove galactosylation and sialylation to be viable indicators of age-related physiological changes.  

In addition, the ratio of 2 glycans can also predict and inform age-related health decline. To that end, researchers have developed an age-related biomarker named GlycoAgeTest that measures the ratio between 2 fucosylated biantennary oligosaccharides (N2GAF:NA2F). The test revealed a gradual increase in the ratio after 40, with implications for dementia (21).  

Differential glycosylation has also been demonstrated in Type 2 diabetes mellitus, metabolic syndrome, cardiovascular diseases, and chronic inflammation (22). Therefore, timely detection of glycan alterations is necessary, as it can help us take preventative measures against the debilitating effects of age-related conditions.  

Analytical Techniques for Detecting Glycan Biomarkers  

We need to employ several complementary methods to establish glycans as efficient biomarkers. These methods exploit the specificity of glycan-binding agents, such as lectins and antibodies, to deduce the diagnostic value of various glycans. 

Flow cytometry and mass spectrometry can be used to analyze glycan content in liquid biopsies using glycan binders (23,24). On the other hand, genome-wide glycan profiling requires high-throughput methods, such as glycan arrays, which can identify several glycans with their binding partners and quantify binding using fluorescence-based detection (25).

Quantitative methods can be complemented with immunohistochemistry and immunofluorescence that help visualize the subcellular glycan distribution and impact on cell phenotype. Lastly, capillary electrophoresis augments specificity by allowing the separation of isomers—glycans harboring the same monosaccharide sequence but different conformations (26). 

Conclusion 

The strong correlation between disease and altered glycan profile is evident. More importantly, detailed glycan profiling reveals a heterogeneous patient profile in many diseases, especially cancer. Current research must address some of the hurdles in glycan discovery, such as detecting previously-unknown complex glycan sequences, differentiating between benign and malignant types, and elucidating the downstream effects of aberrant glycosylation.

Nevertheless, researchers continue to generate novel insights thanks to the expanding knowledge of glycan binders and the combination of analytical techniques. Every milestone in this area will contribute to the early detection and detailed profiling of cancer subtypes, which is necessary for predicting the best treatment option for every patient.  

Comprehensive glycobiology expertise is a must to achieve milestones in glycan biomarker discovery. That’s why we offer not only a large selection of lectins as detection kits but also educational resources. A recent article, “Leveraging Glycobiology for Biomarker Discovery”, from Pamela James, Vice President, Product at Vector Laboratories offers a concise outlook on glycobiology and its importance in drug discovery. In addition, you can refer to the Intro to Glycobiology ebook and SpeakEasy Science blog to get a better idea of how we can assist with your research questions. 

 References

1. Yin BWT, et al. 2001. Molecular Cloning of the CA125 Ovarian Cancer Antigen: Identification as a New Mucin, MUC16. The Journal of Biological Chemistry. 
2. Adamczyk B, et al. 2012. Glycans as Cancer Biomarkers. Biochimica et Biophysica Acta. 
3. Duffy MJ, et al. 2000. CA 15-3: A Prognostic Marker in Breast Cancer. The International Journal of Biological Markers. 
4. Ahrens TD, et al. 2020. The Role of Proteoglycans in Cancer Metastasis and Circulating Tumor Cell Analysis. Frontiers in Cell and Developmental Biology. 
5. Cooney CA, et al. 2011. Chondroitin Sulfates Play a Major Role in Breast Cancer Metastasis: A Role for CSPG4 and CHST11 gene Expression in Forming Surface P-Selectin Ligands in Aggressive Breast Cancer Cells. Breast Cancer Research. 
6. Sipione S, et al. 2020. Gangliosides in the Brain: Physiology, Pathophysiology and Therapeutic Applications. Frontiers in Neuroscience. 
7. Sarbu M, et al. 2022. Gangliosides as Biomarkers of Human Brain Diseases: Trends in Discovery and Characterization by High-Performance Mass Spectrometry. International Journal of Molecular Sciences. 
8. Thomas D, et al. 2021. Altered Glycosylation in Cancer: A Promising Target for Biomarkers and Therapeutics. Biochimica et Biophysica Acta (BBA)-Reviews on Cancer. 
9. Ishikawa T, et al. 2017. An Automated Micro-Total Immunoassay System for Measuring Cancer-Associated α2,3-linked Sialyl N-Glycan-Carrying Prostate-Specific Antigen May Improve the Accuracy of Prostate Cancer Diagnosis. International Journal of Molecular Sciences. 
10. Ralhan R, et al. 2008. Discovery and Verification of Head-and-Neck Cancer Biomarkers by Differential Protein Expression Analysis Using iTRAQ Labeling, Multidimensional Liquid Chromatography, and Tandem Mass Spectrometry. Molecular & Cellular Proteomics. 
11. Chai AB, et al. 2019. P‐Glycoprotein: A Role in the Export of Amyloid‐β in Alzheimer’s Disease? The FEBS Journal. 
12. Kao YH, et al. 2015. Regulation of P-Glycoprotein Expression in Brain Capillaries in Huntington’s Disease and Its Impact on Brain Availability of Antipsychotic Agents Risperidone and Paliperidone. Journal of Cerebral Blood Flow & Metabolism. 
13. Khare P, et al. 2018. Myelin Oligodendrocyte Glycoprotein-Specific Antibodies From Multiple Sclerosis Patients Exacerbate Disease in a Humanized Mouse Model. Journal of Autoimmunity. 
14. Bronge M, et al. 2019. Myelin Oligodendrocyte Glycoprotein Revisited—Sensitive Detection of MOG-Specific T-Cells in Multiple Sclerosis. Journal of Autoimmunity. 
15. Dunn AR, et al. 2017. Synaptic Vesicle Glycoprotein 2C (SV2C) Modulates Dopamine Release and Is Disrupted in Parkinson Disease. Proceedings of the National Academy of Sciences. 
16. Moloney EB, et al. 2018. The Glycoprotein GPNMB Is Selectively Elevated in the Substantia Nigra of Parkinson’s Disease Patients and Increases After Lysosomal Stress. Neurobiology of Disease. 
17. Papuc E, et al. 2016. Humoral Response Against Myelin Associated Glycoprotein in Parkinson’s Disease Reflect Oligodendroglial Degeneration. Annals of Agricultural and Environmental Medicine. 
18. Ma L, et al. 2019. Role of Microtubule-Associated Protein 6 Glycosylated With Gal-(β-1, 3)-GalNAc in Parkinson’s disease. Aging. 
19. Vanhooren V, et al. 2007. N-Glycomic Changes in Serum Proteins During Human Aging. Rejuvenation Research. 
20. Ding N, et al. 2011. Human Serum N-Glycan Profiles Are Age and Sex Dependent. Age and Ageing. 
21. Vanhooren V, et al. 2010. Serum N-Glycan Profile Shift During Human Ageing. Experimental Gerontology. 
22. Paton B, et al. 2021. Glycosylation Biomarkers Associated With Age-Related Diseases and Current Methods for Glycan Analysis. International Journal of Molecular Sciences. 
23. Tucker-Burden C, et al. 2012. Lectins Identify Glycan Biomarkers on Glioblastoma-Derived Cancer Stem Cells. Stem Cells and Development. 
24. de Leoz MLA, et al. 2008. Glycomic Approach for Potential Biomarkers on Prostate Cancer: Profiling of N-Linked Glycans in Human Sera and pRNS Cell Lines. Disease Markers. 
25. Purohit S, et al. 2018. Multiplex Glycan Bead Array for High Throughput and High Content Analyses of Glycan Binding Proteins. Nature Communications. 
26. Lu G, et al. 2018. Capillary Electrophoresis Separations of Glycans. Chemical Reviews. 

The human body is home to a multitude of glycans attached to different macromolecules in various locations in the cell and within the cellular environment. This mind-blowing diversity makes the complete analysis of glycans challenging and calls for robust glycan-recognizing probes.

Fortunately, the last 2 decades witnessed the discovery of macromolecules with useful glycan-binding features, such as plant lectins, antibodies, and proteins with carbohydrate-binding modules (CBMs). These tools can be isolated and utilized with many characterization techniques. Furthermore, advancements in synthetic biology are paving the way for more diverse and sensitive probes.

Classification of Glycan-Binding Proteins

Lectins

Lectins are a broad class of proteins known for their ability to bind carbohydrates. While the endogenous lectins interact with glycans to mediate several essential biological processes, lectins from other sources have an equally important quest: detecting and quantifying glycans.

Plant Lectins

Plant lectins are currently the gold standard for lectin-based analyses. Many plant lectins are abundant in nature and easy to extract. Furthermore, they display highly specific and reversible carbohydrate-binding activity, which makes them versatile glycan recognition tools. They have been employed in glycan analysis of various cancers, viral diseases, and autoimmune disorders (1).

Recent research went a step further and used plant lectins as therapeutics and for enhanced targeting of cancer and infectious diseases (2). You can gain invaluable insight into the various applications of plant lectins in detection and treatment by taking a look at the SpeakEasy science blog.

Recombinant Lectins

Despite their wide adoption in glycan analysis, isolating plant lectins in pure forms and high biomasses remains a challenge. Synthetic biology, particularly recombinant DNA technology, has been proposed for tackling these challenges. Researchers use a range of host cells, including bacteria, yeast, and mammalian cells, to express tailor-made plant lectins using lectin cDNA libraries. The resulting recombinant lectins are reported to have less contamination and less batch-to-batch variation than their natural counterparts, driving better accuracy and reproducibility.

Oliveira et al. provided a comprehensive review of strategies and applications involving recombinant lectins, highlighting the advantages and disadvantages of every host (3).

Improving Natural Lectin Specificity

​​In the meantime, several other methods have been established for enhancing the natural lectin specificity towards glycans. Site-directed mutagenesis involves inducing a mutation in 1 or more amino acid residues to augment specificity.

Another option is to apply randomized mutations to create a large lectin library. One study employed mutagenesis to Peanut agglutinin by mutating its Asn41 residue, which enhanced its specificity towards the tumor-associated TF antigen (Galβ1−3GalNAc) (4). Another study endowed galactose binding to a naturally mannose-binding lectin by mutagenesis at multiple residues (5). ​

Besides mutating the existing binding site, adding new binding sites to a lectin has been explored. These lectins became multivalent, significantly improving their binding affinities to the respective glycans (6).

Antibodies

Another class of glycan-recognizing probes stems from our immune system, produced by B cells through repeated exposure to foreign substances, especially microbes. These endogenous glycan-binding antibodies serve critical protective functions by recognizing surface glycans of pathogenic bacteria, viruses, and fungi. Other endogenous antibodies are responsible for recognizing tumor-associated carbohydrate antigens and can trigger an immune response.

Recombinant Antibody

A newly emerging field in anti-glycan antibodies is the use of recombinant DNA technology. More specifically, one can generate a large library of antibody-encoding genes, which are then introduced into host cells to yield the set of antibodies. The resulting antibody library can subsequently be used to select and amplify the antibodies with the highest affinity towards the antigen of interest.

The recombinant antibody technology can be particularly beneficial for maximizing the efficacy of antibodies. For example, Amon et al. used a yeast surface display to design an antibody library against sialyl Lewis a (SLea) for the treatment of colon and pancreatic cancers (7). The library of mutant antibodies displayed higher cytotoxicity towards SLea-positive cancer cells than the native SLea antibody.

Researchers from Harvard Medical School followed a similar approach to generate an antibody-encoding gene library from sea lamprey, which yielded much higher glycan specificity than conventional mouse monoclonal antibodies.

Carbohydrate-Binding Modules (CBMs)

Carbohydrate-active enzymes are responsible for building or degrading complex carbohydrates. Research revealed that these enzymes carry 1 or more domains called carbohydrate-binding modules (CBMs that can target the enzyme to the polysaccharide by recognizing specific carbohydrates.

Today, protein engineering enables heterologous expression of CBMs in large quantities. Thus, an intriguing alternative to antibodies and lectins is the use of synthetic biology to engineer CBMs for glycan recognition purposes.

Characterization of Glycan-Binding Proteins (GBP)

The proper characterization of glycan-binding proteins is necessary for the applications mentioned above to succeed. Researchers should evaluate and confirm the binding characteristics of proteins, especially in the case of engineered proteins.

Surface Plasmon Resonance (SPR) and titration calorimetry are the most widely used methods of GBP characterization without disrupting molecular structures (8).

Surface Plasmon Resonance (SPR)

SPR involves immobilizing the glycan of interest on a metal surface and measuring glycan binding through mass changes deduced from the electromagnetic oscillation frequency of the surface electrons. It allows a real-time estimate of the binding interaction, which provides quantitative insight, such as association and dissociation constants. However, SPR requires sufficient glycan attachment to the surface for precise measurements.

Isothermal Titration Calorimetry (ITC)

Isothermal titration calorimetry (ITC estimates glycan binding from changes in heat induced by binding, which helps deduce enthalpy, entropy, and binding constants. It has particularly been the preferred method for measuring binding affinities in mutant/altered GBPs and comparing their binding properties to the wild types. While it eliminates the need for glycan immobilization, it is not suitable for high-throughput characterization since an ITC chamber can take 1 GBP-glycan interaction at a time.

Glycan Microarray

One can generate a better high-throughput readout using glycan microarrays comprising various simple and complex carbohydrates immobilized on a plate. This allows the simultaneous characterization of multiple lectins or antibodies for binding specificity.

What makes glycan microarrays stand out amongst other methods is the availability of large glycan microarray libraries and toolkits curated from experimental data, enabling visualization and analysis of glycan microarray datasets. Among the most widely-used analysis platforms of glycan microarray datasets are CarboGrove and GlycoToolKit – Glycan Array Dasboard (GLAD). Also, Haab et al. elaborates on many other resources and step-by-step implementation of glycan microarrays (9).

Applications of Glycan-Binding Proteins

Each of the glycan-recognizing probes described above has distinct advantages that help boost glycan analysis applications, including but not limited to antibody- or lectin-based microarrays, immunoassays (e.g., ELISA), cell sorting, purification of glycoconjugates or glycans for further analysis, glycosyltransferase kinetic assays, immunofluorescence (IF), and immunohistochemistry (IHC).

Each of these applications uncovers different insights about glycobiology. Glycan and glycoprotein purification through affinity chromatography help identify post-translational protein modifications and prevalent glycan epitopes on these proteins. This can be beneficial when determining the mutant glycans on altered and diseased samples. On the other hand, IHC/IF approaches help characterize surface glycoconjugates, revealing their cellular locations, spatial orientations, and relative abundance in cells.

More recent approaches involve cloning glycosyltransferase genes. For example, researchers can identify novel genes encoding specific glycosyltransferases and transfect cell lines with those genes to characterize their functions. Enzyme function, quantity, and activity can also be deduced using lectins and antibodies.

Lectin Applications

Lectins are widely used as tumor biomarker detectors, where they detect the unique glycan epitopes present in malignant cancer cells. This helps distinguish cancer subtypes more accurately, determining the stage, metastatic potential, and aggressiveness. Lectin microarray consists of a panel of lectins and it is the preferred strategy for accelerated high-throughput biomarker discovery.

For example, lectin microarrays were used to characterize triple-negative breast cancer (TNBC), which constitutes 15% of breast cancer cases and is characterized by its metastatic and treatment-resistant nature. Using lectin microarrays on 6 TNBC cell lines, researchers identified Ricinus communis agglutinin I as the lectin that could detect the aberrant surface glycans on TNBC (10).

Lectins can be incorporated into IHC/IF applications to understand the glycosylation state to visualize and analyze tumor microenvironment as well as disease characteristics such as cancer progression and metastatic behavior. Shuhui Chen, PhD and Erika Leonard, PhD from Vector Laboratories applied a set of plant lectins to different cancerous tissues to characterize differential glycosylation states across cancer types. Their work demonstrated the utility of plant lectins for robust cancer biomarker discovery.

Besides diagnostic potential, many plant lectins exhibit anti-cancer activity. One of them is a mannose-binding lectin from Pinellia pedatisecta that triggers cancer cell death in lung cancer and hepatocellular carcinoma (11). Another mannose- and glucose-specific lectin isolated from the seeds of Dioclea sclerocarpa intervened in cancer cell cycles at immune checkpoints in ovarian and prostate cancer (12).

Glycan-Binding Antibody Applications

Besides their intrinsic functions, endogenous anti-glycan antibodies contribute to diagnostics. By quantifying antibodies in patient serum, researchers develop tools to diagnose bacterial infections (13), autoimmune diseases (14), and cancer (15).

The anti-glycan antibody production machinery has also been exploited for vaccine production. Carbohydrate-based vaccines can activate immune response by introducing immunogenic glycans for the body to generate antibodies. Many glycan-based vaccines have been FDA-approved and widely used since the 90s for the prevention of influenza (16), meningitis (17), and pneumonia (17).

Cancer vaccine research is another exciting area that studies glycans to activate the immune response to cancer cells. These vaccines mainly comprise tumor-associated carbohydrate antigens (TACAs), including but not limited to the Tn, TF, and sialyl-Tn antigens (18).

Although these glycans induce weak immunogenicity on their own, this can be overcome by coupling the antigens to other immunogenic molecules, such as carrier proteins. Thus, the vaccine can direct T cell-dependent immune responses to the TACAs (19). There are currently 4 cancer vaccines in phase 3 clinical trials for breast cancer (20–21), non-small cell lung cancer (22), and melanoma (23).

Besides prevention, glycan-binding antibodies have also gained recognition for treatment. Researchers can now synthesize monoclonal antibodies in large quantities for treating infectious diseases and cancer (24). These antibodies act either by binding to cancer cell surface glycans for easier recognition by the immune system or by blocking the glycoproteins responsible for dampening the checkpoint-based immune response.

Carbohydrate-binding Modules (CBMs) Applications

CBMs have been used in glycan identification. Sialidases, responsible for cleaving terminal sialic acid residues, are among the enzymes under investigation. Their sialic acid recognition mechanism, combined with recombinant technology, can help design robust sialic acid-recognizing tools.

One great example is the work of Ribiero et al., who engineered a sialic acid CBM from Clostridium perfringens, which showed higher affinity to α(2,3)-sialyl-lactose than any other natural lectins in the same class. The research team also engineered a dimeric version of the CBM with an added affinity towards 6’-sialyl-lactose (24).

Conclusion

The discovery and development of novel glycan-binding probes is on the rise. Consequently, their applications can diversify and evolve our understanding of glycobiology and its role in health and disease.

Understanding the basics of glycobiology and glycan recognition is fundamental for state of the art glycan analysis. Our SpeakEasy blog is a great place to start, as it contains several informative articles about the basics of glycobiology and applications of glycan-recognizing probes such as lectins. You can also find publication reviews demonstrating the applications of lectins in glycobiology.

In addition, Essentials of Glycobiology is a practical resource for learning the basics of carbohydrates and enhancing your knowledge of advanced glycobiology research.

Lastly, the Alliance of Glycobiologists for Cancer Research, established by the National Cancer Institute, provides a list of additional resources, including glycan databases, projects by the Consortium for Functional Glycomics, and symposiums.

References

1. Bah CSF, et al. 2013. Medicinal Applications of Plant Lectins. Antitumor Potential and other Emerging Medicinal Properties of Natural Compounds.
2. Katoch R, et al. 2021. Research Advances and Prospects of Legume Lectins. Journal of Biosciences.
3. Carla Oliveira JA, et al. 2012. Recombinant Lectins: An Array of Tailor-Made Glycan-Interaction Biosynthetic Tools. Critical Reviews in Biotechnology.
4. Adhikari P, et al. 2001. Mutational Analysis at Asn-41 in Peanut Agglutinin: A Residue Critical for the Binding of the Tumor-Associated Thomsen-Friedenreich Antigen. Journal of Biological Chemistry.
5. Drickamer K. 1992. Engineering Galactose-Binding Activity Into a C-Type Mannose-Binding Protein. Nature.
6. Yabe R, et al. 2009. Engineering a Versatile Tandem Repeat-Type α2-6sialic Acid-Binding Lectin. Biochemical and Biophysical Research Communications.
7. Amon R, et al. 2020. Directed Evolution of Therapeutic Antibodies Targeting Glycosylation in Cancer. Cancers.
8. Warkentin R, et al. 2021. Resources and Methods for Engineering “Designer” Glycan-Binding Proteins. Molecules.
9. Haab BB, et al. 2020. Advances in Tools to Determine the Glycan-Binding Specificities of Lectins and Antibodies. Molecular & Cellular Proteomics.
10. Zhou SM, et al. 2015. Lectin RCA-I Specifically Binds to Metastasis-Associated Cell Surface Glycans in Triple-Negative Breast Cancer. Breast Cancer Research.
11. Lu Q, et al. 2012. Pinellia Pedatisecta Agglutinin Interacts With the Methylosome and Induces Cancer Cell Death. Oncogenesis.
12. Li Ping, et al. 2017. Caspase-9: Structure, Mechanisms and Clinical Application. Oncotarget.
13. Seow CH, et al. 2009. Novel Anti-Glycan Antibodies Related to Inflammatory Bowel Disease Diagnosis and Phenotype. American Journal of Gastroenterology.
14. Brettschneider J, et al. 2009. Serum Anti-GAGA4 IgM Antibodies Differentiate Relapsing Remitting and Secondary Progressive Multiple Sclerosis From Primary Progressive Multiple Sclerosis and Other Neurological Diseases. Journal of Neuroimmunology.
15. Tikhonov A, et al. 2020. Glycan-Specific Antibodies as Potential Cancer Biomarkers: A Focus on Microarray Applications. Clinical Chemistry and Laboratory Medicine.
16. Ahonkhai VI, et al. 1990. Haemophilus Influenzae Type B Conjugate Vaccine (Meningococcal Protein Conjugate) (PedvaxHIB): Clinical Evaluation. Pediatrics.
17. Goldschneider I, et al. 1969. Human Immunity to the Meningococcus: I. The Role of Humoral Antibodies. Journal of Experimental Medicine.
18. Temme JS, et al. 2021. Anti-Glycan Antibodies: Roles in Human Disease. Biochemical Journal.
19. Jin KT, et al. 2019. Recent Advances in Carbohydrate-Based Cancer Vaccines. Biotechnology Letters.
20. Miles D, et al. 2011. Phase III Multicenter Clinical Trial of the Sialyl‐TN (STn)‐Keyhole Limpet Hemocyanin (KLH) Vaccine for Metastatic Breast Cancer. The Oncologist.
21. Huang CS, et al. 2016. Randomized Phase II/III Trial of Active Immunotherapy with OPT-822/OPT-821 in Patients With Metastatic Breast Cancer. Journal of Clinical Oncology.
22. Segatori VI, et al. 2018. Antibody-Dependent Cell-Mediated Cytotoxicity Induced by Active Immunotherapy Based on Racotumomab in Non-Small Cell Lung Cancer Patients. Cancer Immunology, Immunotherapy.
23. Kirkwood JM, et al. 2016. High-Dose Interferon Alfa-2b Significantly Prolongs Relapse-Free and Overall Survival Compared With the GM2-KLH/QS-21 Vaccine in Patients With Resected Stage IIB-III Melanoma: Results of Intergroup Trial E1694/S9512/C509801. Journal of Clinical Oncology.
24. Ribeiro JP, et al. 2016. Characterization of a High-Affinity Sialic Acid-Specific CBM40 From Clostridium Perfringens and Engineering of a Divalent Form. Biochemical Journal.